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      Identification of Online Recruitment Fraud (ORF) through Predictive Models

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            Abstract

            Job postings online have become popular these days due to connecting to job seekers around the world. There are also instances where the fraudulent employer posts a job online and expects people to apply to these postings. These fraudulent employers impend job seekers' privacy, spawns fake job offers, and wanes. We perceived that most of the Online Recruitment Fraud (ORF) has matching features. Though the user cannot categorize them, we propose using various predictive models like Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, Naïve Bayes, or Logistics Regression to detect them effortlessly. Dataset with 17780 job postings was downloaded from Kaggle to identify which proposed model best predicts the fraudulent job posting. The dataset includes 14 features to determine whether online job posting is fraudulent or non-fraudulent. 70% of these job postings train the model, and the remaining 30% test the model's efficiency. The outcomes of each model are predicted using four evaluation metrics – Classification Accuracy (CA), Precision, Recall and F-1 score. The research found its suitability from two sides: the websites can identify fake jobs before being published, and job seekers are sheltered from fraudulent job postings.

            Content

            Author and article information

            Journal
            10.54878/EJBESS
            Emirati Journal of Business, Economics and Social Studies
            EJBESS
            Emirates Scholar
            12 April 2022
            : 1
            : 1
            : 39-51
            Affiliations
            [1 ]City University College of Ajman, United Arab Erimates
            Author notes
            Correspondence: Riktesh Srivastava ( riktesh.srivastava@ 123456gmail.com )
            Article
            10.54878/EJBESS.170
            f9deecd5-9f76-4ca3-819e-16cf31eab416
            ©2022Emirates Scholar

            This is an open access article published by Emirates Scholar and distributed under the Creative Commons Attribution License 4.0 (CC BY).

            History

            Economic theory,Management,Social & Behavioral Sciences,Business & Corporate economics,Economics
            Logistic Regression, Artificial Neural Networks, Online Recruitment Fraud (ORF)., Recall, Stochastic Gradient Descent, Precision, CA

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